291 lines
9.7 KiB
Python
291 lines
9.7 KiB
Python
from modules.patch import patch_all
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patch_all()
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import os
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import einops
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import torch
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import numpy as np
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import fcbh.model_management
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import fcbh.model_detection
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import fcbh.model_patcher
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import fcbh.utils
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import fcbh.controlnet
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import modules.sample_hijack
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import fcbh.samplers
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import fcbh.latent_formats
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from fcbh.sd import load_checkpoint_guess_config
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from nodes import VAEDecode, EmptyLatentImage, VAEEncode, VAEEncodeTiled, VAEDecodeTiled, \
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ControlNetApplyAdvanced
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from fcbh_extras.nodes_freelunch import FreeU_V2
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from fcbh.sample import prepare_mask
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from modules.patch import patched_sampler_cfg_function, patched_model_function_wrapper
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from fcbh.lora import model_lora_keys_unet, model_lora_keys_clip, load_lora
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from modules.path import embeddings_path
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opEmptyLatentImage = EmptyLatentImage()
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opVAEDecode = VAEDecode()
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opVAEEncode = VAEEncode()
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opVAEDecodeTiled = VAEDecodeTiled()
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opVAEEncodeTiled = VAEEncodeTiled()
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opControlNetApplyAdvanced = ControlNetApplyAdvanced()
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opFreeU = FreeU_V2()
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class StableDiffusionModel:
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def __init__(self, unet, vae, clip, clip_vision):
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self.unet = unet
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self.vae = vae
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self.clip = clip
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self.clip_vision = clip_vision
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@torch.no_grad()
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@torch.inference_mode()
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def apply_freeu(model, b1, b2, s1, s2):
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return opFreeU.patch(model=model, b1=b1, b2=b2, s1=s1, s2=s2)[0]
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@torch.no_grad()
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@torch.inference_mode()
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def load_controlnet(ckpt_filename):
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return fcbh.controlnet.load_controlnet(ckpt_filename)
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@torch.no_grad()
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@torch.inference_mode()
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def apply_controlnet(positive, negative, control_net, image, strength, start_percent, end_percent):
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return opControlNetApplyAdvanced.apply_controlnet(positive=positive, negative=negative, control_net=control_net,
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image=image, strength=strength, start_percent=start_percent, end_percent=end_percent)
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@torch.no_grad()
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@torch.inference_mode()
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def load_model(ckpt_filename):
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unet, clip, vae, clip_vision = load_checkpoint_guess_config(ckpt_filename, embedding_directory=embeddings_path)
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unet.model_options['sampler_cfg_function'] = patched_sampler_cfg_function
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unet.model_options['model_function_wrapper'] = patched_model_function_wrapper
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return StableDiffusionModel(unet=unet, clip=clip, vae=vae, clip_vision=clip_vision)
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@torch.no_grad()
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@torch.inference_mode()
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def load_sd_lora(model, lora_filename, strength_model=1.0, strength_clip=1.0):
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if strength_model == 0 and strength_clip == 0:
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return model
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lora = fcbh.utils.load_torch_file(lora_filename, safe_load=False)
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if lora_filename.lower().endswith('.fooocus.patch'):
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loaded = lora
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else:
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key_map = model_lora_keys_unet(model.unet.model)
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key_map = model_lora_keys_clip(model.clip.cond_stage_model, key_map)
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loaded = load_lora(lora, key_map)
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new_unet = model.unet.clone()
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loaded_unet_keys = new_unet.add_patches(loaded, strength_model)
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new_clip = model.clip.clone()
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loaded_clip_keys = new_clip.add_patches(loaded, strength_clip)
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loaded_keys = set(list(loaded_unet_keys) + list(loaded_clip_keys))
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for x in loaded:
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if x not in loaded_keys:
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print("Lora key not loaded: ", x)
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return StableDiffusionModel(unet=new_unet, clip=new_clip, vae=model.vae, clip_vision=model.clip_vision)
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@torch.no_grad()
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@torch.inference_mode()
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def generate_empty_latent(width=1024, height=1024, batch_size=1):
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return opEmptyLatentImage.generate(width=width, height=height, batch_size=batch_size)[0]
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@torch.no_grad()
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@torch.inference_mode()
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def decode_vae(vae, latent_image, tiled=False):
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if tiled:
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return opVAEDecodeTiled.decode(samples=latent_image, vae=vae, tile_size=512)[0]
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else:
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return opVAEDecode.decode(samples=latent_image, vae=vae)[0]
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@torch.no_grad()
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@torch.inference_mode()
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def encode_vae(vae, pixels, tiled=False):
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if tiled:
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return opVAEEncodeTiled.encode(pixels=pixels, vae=vae, tile_size=512)[0]
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else:
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return opVAEEncode.encode(pixels=pixels, vae=vae)[0]
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@torch.no_grad()
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@torch.inference_mode()
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def encode_vae_inpaint(vae, pixels, mask):
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assert mask.ndim == 3 and pixels.ndim == 4
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assert mask.shape[-1] == pixels.shape[-2]
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assert mask.shape[-2] == pixels.shape[-3]
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w = mask.round()[..., None]
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pixels = pixels * (1 - w) + 0.5 * w
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latent = vae.encode(pixels)
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B, C, H, W = latent.shape
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latent_mask = mask[:, None, :, :]
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latent_mask = torch.nn.functional.interpolate(latent_mask, size=(H * 8, W * 8), mode="bilinear").round()
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latent_mask = torch.nn.functional.max_pool2d(latent_mask, (8, 8)).round()
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return latent, latent_mask
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class VAEApprox(torch.nn.Module):
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def __init__(self):
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super(VAEApprox, self).__init__()
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self.conv1 = torch.nn.Conv2d(4, 8, (7, 7))
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self.conv2 = torch.nn.Conv2d(8, 16, (5, 5))
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self.conv3 = torch.nn.Conv2d(16, 32, (3, 3))
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self.conv4 = torch.nn.Conv2d(32, 64, (3, 3))
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self.conv5 = torch.nn.Conv2d(64, 32, (3, 3))
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self.conv6 = torch.nn.Conv2d(32, 16, (3, 3))
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self.conv7 = torch.nn.Conv2d(16, 8, (3, 3))
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self.conv8 = torch.nn.Conv2d(8, 3, (3, 3))
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self.current_type = None
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def forward(self, x):
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extra = 11
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x = torch.nn.functional.interpolate(x, (x.shape[2] * 2, x.shape[3] * 2))
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x = torch.nn.functional.pad(x, (extra, extra, extra, extra))
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for layer in [self.conv1, self.conv2, self.conv3, self.conv4, self.conv5, self.conv6, self.conv7, self.conv8]:
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x = layer(x)
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x = torch.nn.functional.leaky_relu(x, 0.1)
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return x
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VAE_approx_models = {}
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@torch.no_grad()
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@torch.inference_mode()
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def get_previewer(model):
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global VAE_approx_models
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from modules.path import vae_approx_path
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is_sdxl = isinstance(model.model.latent_format, fcbh.latent_formats.SDXL)
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vae_approx_filename = os.path.join(vae_approx_path, 'xlvaeapp.pth' if is_sdxl else 'vaeapp_sd15.pth')
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if vae_approx_filename in VAE_approx_models:
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VAE_approx_model = VAE_approx_models[vae_approx_filename]
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else:
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sd = torch.load(vae_approx_filename, map_location='cpu')
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VAE_approx_model = VAEApprox()
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VAE_approx_model.load_state_dict(sd)
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del sd
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VAE_approx_model.eval()
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if fcbh.model_management.should_use_fp16():
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VAE_approx_model.half()
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VAE_approx_model.current_type = torch.float16
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else:
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VAE_approx_model.float()
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VAE_approx_model.current_type = torch.float32
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VAE_approx_model.to(fcbh.model_management.get_torch_device())
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VAE_approx_models[vae_approx_filename] = VAE_approx_model
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@torch.no_grad()
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@torch.inference_mode()
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def preview_function(x0, step, total_steps):
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with torch.no_grad():
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x_sample = x0.to(VAE_approx_model.current_type)
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x_sample = VAE_approx_model(x_sample) * 127.5 + 127.5
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x_sample = einops.rearrange(x_sample, 'b c h w -> b h w c')[0]
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x_sample = x_sample.cpu().numpy().clip(0, 255).astype(np.uint8)
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return x_sample
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return preview_function
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@torch.no_grad()
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@torch.inference_mode()
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def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sampler_name='dpmpp_2m_sde_gpu',
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scheduler='karras', denoise=1.0, disable_noise=False, start_step=None, last_step=None,
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force_full_denoise=False, callback_function=None, refiner=None, refiner_switch=-1,
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previewer_start=None, previewer_end=None, sigmas=None, noise_mean=None):
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if sigmas is not None:
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sigmas = sigmas.clone().to(fcbh.model_management.get_torch_device())
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latent_image = latent["samples"]
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if disable_noise:
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noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
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else:
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batch_inds = latent["batch_index"] if "batch_index" in latent else None
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noise = fcbh.sample.prepare_noise(latent_image, seed, batch_inds)
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if isinstance(noise_mean, torch.Tensor):
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noise = noise + noise_mean - torch.mean(noise, dim=1, keepdim=True)
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noise_mask = None
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if "noise_mask" in latent:
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noise_mask = latent["noise_mask"]
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previewer = get_previewer(model)
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if previewer_start is None:
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previewer_start = 0
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if previewer_end is None:
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previewer_end = steps
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def callback(step, x0, x, total_steps):
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fcbh.model_management.throw_exception_if_processing_interrupted()
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y = None
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if previewer is not None:
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y = previewer(x0, previewer_start + step, previewer_end)
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if callback_function is not None:
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callback_function(previewer_start + step, x0, x, previewer_end, y)
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disable_pbar = False
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modules.sample_hijack.current_refiner = refiner
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modules.sample_hijack.refiner_switch_step = refiner_switch
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fcbh.samplers.sample = modules.sample_hijack.sample_hacked
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try:
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samples = fcbh.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
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denoise=denoise, disable_noise=disable_noise, start_step=start_step,
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last_step=last_step,
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force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback,
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disable_pbar=disable_pbar, seed=seed, sigmas=sigmas)
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out = latent.copy()
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out["samples"] = samples
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finally:
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modules.sample_hijack.current_refiner = None
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return out
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@torch.no_grad()
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@torch.inference_mode()
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def pytorch_to_numpy(x):
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return [np.clip(255. * y.cpu().numpy(), 0, 255).astype(np.uint8) for y in x]
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@torch.no_grad()
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@torch.inference_mode()
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def numpy_to_pytorch(x):
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y = x.astype(np.float32) / 255.0
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y = y[None]
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y = np.ascontiguousarray(y.copy())
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y = torch.from_numpy(y).float()
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return y
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